(1) Field of the Invention
The present invention relates to a system and a method for recognizing patterns which has particularly utility in the field of combat system technology and to the area of signal processing, feature extraction and classification.
(2) Description of Prior Art
In a conventional pattern recognition system, the task to be performed is divided into three phases: data acquisition; data preprocessing; and decision classification. FIG. 1 is a schematic representation of a conventional pattern recognition system. In the data acquisition phase 10, analog data from the physical world are gathered through a transducer and converted to digital format suitable for computer processing. More particularly, the physical variables are converted into a set of measured data, indicated in FIG. 1 by electric signals, x(r), if the physical variables are sound (or light intensity) and the transducer is a microphone (or photocells). The measured data is then used as the input to the second phase 12 (data preprocessing) and is grouped in a third phase 14 into a set of characteristic features, P(i), as output. The third phase 14 is actually a classifier or pattern recognizer which is in the form of a set of decision functions.
Signal classification or pattern recognition methods are often classified as either parametric or nonparametric. For some classification tasks, pattern categories are known a priori to be characterized by a set of parameters. A parametric approach is to define the discriminant function by a class of probability densities by a relatively small number of parameters. There exist many other classifications in which no assumptions can be made about the characterizing parameters. Nonparametric approaches are designed for those tasks. Although some parameterized discriminant functions, e.g. the coefficients of a multivariate polynomial of some degree, are used in nonparametric methods, no conventional form of the distribution is assumed.
In recent years, one of the nonparametric approaches for pattern classification is neural network training. In neural network training for pattern classification, there are a fixed number of categories (classes) into which stimuli (activation) are to be classified. To resolve it, the neural network first undergoes a training session, during which the network is repeatedly presented a set of input patterns along with the category to which each particular pattern belongs. Then later on, there is presented to the network a new pattern which has not been presented to it before but which belongs to the same population of patterns used to train the network. The task for the neural network is to classify this new pattern correctly. Pattern classification as described here is a supervised learning problem. The advantage of using a neural network to perform pattern classification is that it can construct nonlinear decision boundaries between the different classes in nonparametric fashion, and thereby offer a practical method for solving highly complex pattern classification problems.
Signal classification involves the extraction and partition of feature of targets of interest. In many situations, the problem is complicated by the uncertainty of the signal origin, fluctuations in the presence of noise, the degree of correlation of multi-sensor data, and the interference of the nonlinearities in the environment. Research and studies in the past have focused on developing robust and efficient methods and devices for recognizing patterns in signals, many of which have been developed from traditional signal processing techniques, and known artificial neural network technology. There still remains however a need for a system and a method for providing high classification performance.